Statistical facility event monitor

ABSTRACT

A statistical facility event monitor has a computer implemented event percentile meter. The event percentile meter counts the number of randomly initiated events that cause a monitored facility to consume a monitored utility over a monitored time period. The event percentile meter then calculates a cumulative distribution function for the randomly initiated events. The event percentile meter uses the cumulative distribution to determine the event percentile for the monitored facility. The event percentile meter then outputs the event percentile.

FIELD OF THE INVENTION

Embodiments of the present invention relate to statistical measurement.

BACKGROUND OF THE INVENTION

FIG. 1 illustrates a prior art distributed facility management system100. FIG. 1 is based on modified versions of FIGS. 1 and 3 of U.S. Pat.No. 6,816,811 “Method of Intelligent Data Analysis to Detect AbnormalUse of Utilities in Buildings” (Seem). The prior art system comprises acentral computer system 102 and a communications network 110 incommunication with a plurality of building management systems 112located in a plurality of facilities 104, 106 and 108. The system readsin utility data from one or more utility monitors 114 in a building. Thesystem then classifies said utility data into normal 122 and anomaly 124data. In order to determine if data is normal or an anomaly, the systemcomputes an extreme studentized deviate based on the value of each datapoint and the mean and standard deviation for the set of data read infrom the particular monitor in the particular monitored building. Apercentile for each data point is computed for each extreme studentizeddeviate. If the percentile is extreme, then the data point ischaracterized as an anomaly. As used herein, a “percentile” is a valueof a cumulative distribution that indicates what percentage of areference population used to determine the cumulative distribution isless than a given value. Percentile may be expressed as a percentage(e.g. 0 to 100) or a fraction (e.g. 0 to 1.0). It may also be expressedas an equal division of a whole, such as a quartile, quintile or higherorder division.

The Seem system has a number of significant flaws. The extremestudentized deviate is based on the assumption that the data arenormally distributed random numbers which are independent of each other.This is clearly not the case for the examples provided in Seem. The datacharacterized as normal 122 by Seem is not random, but periodic. HenceSeem's fundamental assumption of random and independent data is nottrue. This may account for the false warnings Seem experiences (Seemcolumn 4 line 33). An additional flaw of Seem is that there is no way tocompare the utility consumption of one building to another. Building 104might be a residential facility. Building 106 might be a retailfacility. Building 108 might be a manufacturing facility. One would notexpect facilities in these different facility classes (e.g. industrialclasses) to have comparable utility consumption patterns. Even if allfacilities were in the same facility class, however, it still would notbe possible to compare facilities of substantially different sizes 125.Two apartment buildings, for example, might have very different utilityusage patterns if one had 2 apartments and the other had 100 apartments.

There is a need, therefore, for a system and method for comparingdifferent facilities to determine how the facilities compare to a normrelative to their facility class and size.

SUMMARY OF THE INVENTION

The summary of the invention is provided as a guide to understanding theinvention. It does not necessarily describe the most generic embodimentof the invention or the broadest range of alternative embodiments.

The inventions described herein are broadly applicable. To illustratethe range of applicability of said inventions, examples in the field offuel consumption monitoring as well as examples in the field of lostwages and medical expenses monitoring (e.g. workers' compensationlosses) are described herein.

FIG. 2 is a schematic of a system 200 for statistical facilitymonitoring. The system comprises a computer implemented consumptionpercentile meter 202. The consumption percentile meter reads in 211utility consumption data from a monitored facility 210 over a monitoredtime period 217. As used herein, a “monitored facility” may comprise asingle building, a plurality of buildings (e.g. an office complex),regions within buildings (e.g. an apartment) and outdoor locations (e.g.an area being landscaped). As used herein, “reads in” may include datatransmitted directly from a device and data entered manually into asystem. All communications described herein may be via electroniccommunication systems such as the Internet, WAN, cell phone systems,hard-wired systems, and machine-to-machine (M2M) systems.

The consumption percentile meter also reads in 251 data suitable fordetermining a utility class 216 for said utility being monitored, afacility class 214 for said monitored facility, a temporal class 213 forsaid utility being monitored, a duration 215 of the monitored timeperiod 217, a size 212 for said monitored facility and an expectedaverage unit consumption rate 219 for said monitored utility.

A utility class says what type of utility is being monitored. A utilityclass may include “energy” (e.g. electricity, gas, coal, etc.),“materials” (e.g. raw materials and prefabricated sub-assemblies),“labor” 223, “capital costs” (e.g. construction, depreciation andmaintenance), “insurance losses” (e.g. lost wages and medical expensesdue to on-the-job injuries), and “monetary expenses” (e.g. dollars).Anything that is consumed over time may be described by a utility class.Units of consumption may be standardized within a utility class.Electricity and gas consumption, for example, may be standardized asenergy units, such as kilowatt-hours. Raw materials and subassemblyconsumption may be standardized using monetary units, such as dollars.Labor may be standardized by time and pay rate, such as $/hr. Automatedlabor monitoring may be done with time clocks or other monitors ofactivity, such as workstation usage.

A facility class says what type of facility is being monitored (e.g.apartment, office, factory). Facility class may be determined by anindustrial classification code for a facility (e.g. SIC or NAICS code).

The temporal class of a monitored utility indicates how the rate ofconsumption of the monitored utility varies over time in a monitoredfacility. Temporal classes include “steady consumption” (e.g. heateruse), “periodic consumption” (e.g. daily heating cycle), “randomlyinitiated events” (e.g. backup generator usage due to loss of electricutility power) and other more complicated usage patterns such as “finitestate machine cycles” (e.g. washing machine power draw). Randomlyinitiated events may be characterized by a probability of a random eventoccurring in a given time period and a distribution of magnitudes ofconsumption of a utility associated with each random event. Thedistribution of magnitudes may have an average value.

The monitored time period may be any time period of interest. Theduration of the monitored time period is how long the monitored timeperiod lasts. As will be indicated below, consumption of a monitoredutility may be triggered during a monitored time period and continuepast the end of the monitored time period. The period of additionalconsumption is called a tail period. The consumption during the tailperiod may be attributed to the monitored time period since the eventthat initiated the consumption occurred during the monitored timeperiod. An example is fuel consumption for a backup generator. Thebackup generator may be initiated during a monitored time period due toelectric power loss to the monitored facility. The fuel consumption ofthe backup generator may proceed past the end of the monitored timeperiod and into a tail period depending upon how quickly electric poweris restored to the monitored facility. Nonetheless, the fuel consumptionby the backup generator during the tail period will be attributed to themonitored utility consumption during the monitored time period.

The size of the monitored facility is any physical parameter thatindicates how much the expected consumption of the monitored utilitywill be for the monitored facility. Size can be based on one or more ofphysical size (e.g. square meters), capacity (e.g. power rating), andnumber of facility occupants (e.g. employees).

The expected average unit consumption rate of a monitored utility is theaverage unit rate of utility consumption for reference facilities in thesame facility class and other classes (e.g. temporal class) as themonitored facility. To determine the average unit consumption rate for aset of reference facilities, each reference facility is monitored for amonitored time period. The measured utility consumption for eachreference facility is then divided by the respective durations of themonitored time periods. This gives a consumption rate for each facility.The consumption rates are then summed and divided by the total combinedsizes of all of the reference facilities. This gives an expected averageunit consumption rate. Table 1 below gives a hypothetical example.

TABLE 1 Expected Average Unit Consumption Rate for Backup Generators forHospitals Temporal Class: Randomly Initiated Events Monitored TimePeriod Consumption Size Consumption Duration rate (# Facility (gallons)(years) (gallons/year) beds) A 10000 1 10000 1000 B 12000 1 12000 800 C8000 1 8000 1200 D 8500 1 8500 900 E 13000 1 13000 1100 Total 51500 5000Expected average unit consumption 10.3 rate (gallons/bed/year)

In this example, the facility class is “hospitals”. The utility class is“backup generator fuel”. The temporal class is “randomly initiatedevents”. The size of each facility is based on the number of beds in thehospital. The monitored time periods are all 1 year. The consumptionrate for each hospital is the total consumption of the monitored utilityover each monitored time period divided by the duration of eachmonitored time period. The total consumption rate for all of thereference hospitals is the sum of the consumption rates for eachhospital (e.g. 51500 gal/yr). The total size of all of the hospitals isthe sum of all of the numbers of beds (e.g. 5000). The expected averageunit consumption rate for the reference hospitals, therefore, is 10.3gal/yr/bed.

In an additional refinement, different rooms within a hospital could becategorized into different types. These might include patient rooms,operating theatres, office space, etc. The individual power consumptionfor each room could be monitored during backup generator use. This canbe converted to gallons of backup fuel used by each room using thespecific output of the backup generator (e.g. kwh/gallon). The expectedaverage unit consumptions for each room type within a hospital,therefore, could be calculated.

In a workers' compensation example, the utility being monitored islosses due to employee accident and injury. Each employee may becharacterized by a workers' compensation labor class (e.g. officeworker, maintenance worker, driver, etc.) Each labor class may have anassociated expected average unit consumption rate of losses. This isalso known as “expected loss rate”. Office workers, for example, mighthave an expected loss rate of $0.30/$100 in payroll. The expectedconsumption of the monitored utility (e.g. losses) for each labor classwould be the expected loss rate for each labor class times the payrollof all employees in said labor class. The total expected consumption forthe monitored facility would be the sum of all of the expectedconsumptions for all of the labor classes.

Normalized Consumption

After the consumption percentile meter reads in 211 utility consumptiondata and the other data 251 related to the classes of the monitoredfacility, it determines a normalized consumption for the facility. Thenormalized consumption is the measured consumption for the monitoredfacility divided by the expected consumption for the monitored facility.The expected consumption for the monitored facility is the expectedaverage unit consumption rate times the size of the facility times theduration of the monitored time period. If the monitored facility iscomposed of sub-facilities then the expected average consumption foreach sub-facility is calculated and the total expected consumption ofthe monitored facility is set equal to the sum of the expected averageconsumptions for all of the sub-facilities. As used herein,“sub-facilities” includes workers' compensation labor classes.

For the temporal class of randomly initiated events the expectedconsumption can be calculated from:

-   -   a) a probability of said randomly initiated events occurring per        unit time;    -   b) an average value for a distribution of magnitudes of        consumption of said utility due to each randomly initiated        event; and    -   c) a duration of a monitored time period.

The expected consumption of the monitored utility is equal to theprobability of the randomly initiated events occurring per unit time,times the average value of the distribution of magnitudes, times theduration of the monitored time period. If the probability of saidrandomly initiated events occurring per unit time is also expressed perunit size of said monitored facility, then the probability is multipliedby the size of the monitored facility.

Cumulative Distribution Data

Referring again to FIG. 2, the consumption percentile meter 202 may thenquery 201 a cumulative distribution database 204 to identify and read in203 appropriate cumulative distribution data (CDF) 206. The consumptionpercentile meter may use one or more of the utility class, facilityclass, temporal class, duration of the monitored time period, and anexpected consumption quantity class to identify the appropriatecumulative distribution data. The cumulative distribution data providesa consumption percentile versus normalized consumption. The consumptionpercentile is the percent of reference facilities in the same facilityclass, etc., as the monitored facility that have a normalized utilityconsumption that is less than or equal to a given value. Percentile canbe expressed as percent (e.g. 0 to 100) or fraction (e.g. 0 to 1.0).

The cumulative distribution data may be determined from a set ofreference facilities (e.g. 220, 230, 240) in the appropriate classes. Inthe hospital example above, the monitored utility consumption for thefive reference hospitals could be used to develop cumulativedistribution data for the facility class of “hospitals”. Each referencefacility provides utility consumption data (e.g. 221, 231, 241) to thecumulative distribution database 204. The cumulative distributiondatabase then calculates a normalized consumption for each referencefacility. The normalized consumption is the ratio of the measuredconsumption to the expected consumption. The expected consumption isbased on the size of each reference facility, the duration of eachmonitored time period and the expected average unit consumption rate forall of the reference facilities. The normalized consumptions for thereference facilities are then sorted (e.g. low to high) to determine(e.g. 222, 232, 242) a consumption percentile associated with eachnormalized consumption. The consumption percentile versus normalizedconsumption then becomes the cumulative distribution data. Table 2 showsan example determination of cumulative distribution data for thehospital example in table 1.

TABLE 2 Cumulative Distribution Data for Backup Generators for HospitalsTemporal Class: Randomly Initiated Events Monitored Time ConsumptionPeriod rate Expected Consumption Consumption Duration (gallon SizeConsumption Normalized Percentile Facility (gallons) (years) s/year) (#beds) (gallons) Consumption (%) C 8000 1 8000 1200 12360 0.65 20 D 85001 8500 900 9270 0.92 40 A 10000 1 10000 1000 10300 0.97 60 E 13000 113000 1100 11330 1.15 80 B 12000 1 12000 800 8240 1.46 100 Total 515005000 Expected average unit 10.3 consumption rate (gallons/bed/year)

The facilities have been sorted from low normalized consumption to highnormalized consumption and a consumption percentile has been assigned toeach normalized consumption. The consumption percentile is 100 dividedby the number of reference facilities times the rank order of eachreference facility. Facility C, for example, has a rank order of 1.Facility D has a rank order of 2, and so on. The last two columns intable 2, therefore, are an example of cumulative distribution data.

Reference facilities used to create cumulative distribution data may belimited to a range of expected consumptions. This range is called anexpected consumption quantity class. Cumulative distribution data willbe more accurate if the reference facilities used to develop thecumulative distribution data all have expected consumptions within agiven range. The range should be large enough so that there is anadequate number of reference facilities to develop the cumulativedistribution data. An adequate number of reference facilities is 100 ormore. In the field of workers' compensation, the expected consumptionquantity classes are called “Expected Ultimate Loss Groups”. ExpectedUltimate Loss Groups each have a maximum and a minimum. The ratios ofthe maximums to the minimums are generally in the range of 1.06 to 1.6.Table M, provided by the National Council on Compensation Insurance, hascumulative distribution data for different Expected Ultimate LossGroups.

A surprising advantage of using expected consumption quantity classes isthat cumulative distribution data developed for reference facilitieswith a given duration of their monitored time periods can be used formonitored facilities with a different duration of their monitored timeperiods. In the hospital example above, the duration of the monitoredtime periods of the reference facilities is one year. The expectedconsumption quantity class is 8,000 gallons to 13,000 gallons. Thecumulative distribution data for these reference hospitals could be usedfor a smaller hospital if the monitored time period were long enough sothat the expected consumption of the smaller hospital was in the rangeof 8,000 gallons to 13,000 gallons. For example, if the monitoredhospital had an expected consumption rate of 3,000 gallons per year andthe monitored time period was 3 years, the expected consumption of thesmaller hospital for the monitored time period would be 9,000 gallons.This is in the range of 8,000 gallons to 13,000 gallons and hence thecumulative distribution data from these reference hospitals could beused.

Consumption Percentile for a Monitored Facility

After the consumption percentile meter reads in 203 the appropriatecumulative distribution data, it may then determine a consumptionpercentile 209 for the monitored facility 210 based on the normalizedconsumption 207 of the monitored facility and the cumulativedistribution data 206. The consumption percentile meter may then output262 the consumption percentile of the monitored facility.

The consumption percentile meter gives a user an indication of how theutility consumption of a monitored facility compares to the referencefacilities in one or more of the same classes. Thus, it has substantialutility over and above the prior art Seem system. It can be used todetermine if the utility consumption of a monitored facility hasdeparted from what is normal for its peers (e.g. the referencefacilities). This may indicate a physical problem with the monitoredfacility, such as a need for maintenance.

The consumption percentile meter may be further useful for billingpurposes. A facility may be billed based on how its consumption stacksup against its peers as opposed to being based directly on theconsumption itself. A computer implemented facility billing module 260may be provided. The facility billing module reads in the consumptionpercentile and generates a charge 264 for the monitored utility. Thecharge may be based on the consumption percentile and the expectedconsumption of the monitored facility. Examples are provided below.

The consumption percentile meter may also contribute 205 its normalizedconsumption data to the cumulative distribution database forincorporation into the cumulative distribution data. Thus, a monitoredfacility may also be a reference facility.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a schematic of a prior art distributed facility managementsystem.

FIG. 2 is a schematic of a system for statistical facility monitoring.

FIG. 3 is a flow chart of a method for consumption percentile metering.

FIG. 4 is a graph comparing cumulative distributions of facilities indifferent expected consumption quantity classes.

FIG. 5 is a graph of an exemplary normalized rate curve versusconsumption percentile.

FIG. 6 is a graph of an exemplary normalized rate curve versusconsumption percentile.

FIG. 7 is a graph of an exemplary normalized rate curve versusconsumption percentile.

FIG. 8 is a flow chart of a method for consumption percentileforecasting.

FIG. 9 is a graph of forecasting charges based on intermediateforecasted consumption percentiles.

FIG. 10 is a flow chart of a method for event percentile metering.

DETAILED DESCRIPTION

The detailed description describes non-limiting exemplary embodiments.Any individual features may be combined with other features as requiredby different applications for at least the benefits described herein. Asused herein, the term “about” means plus or minus 10% of a given valueunless specifically indicated otherwise.

A portion of the disclosure of this patent document contains material towhich a claim for copyright is made. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent file or records, but reserves all other copyright rightswhatsoever.

As used herein, a “computer implemented system” or the like comprises aninput device for receiving data, an output device for outputting data intangible form (e.g. printing or displaying on a computer screen), apermanent memory for storing data as well as computer code, and adigital processor for executing computer code wherein said computer coderesident in said permanent memory will physically cause said digitalprocessor to read-in data via said input device, process said datawithin said digital processor and output said processed data via saidoutput device. Said digital processor may be a microprocessor. Saiddigital processor and permanent memory may have distributed forms, suchas cloud-based processing or storage.

Method of Consumption Percentile Metering

FIG. 3 is a flow chart 300 of an exemplary method for consumptionpercentile metering. A consumption percentile meter (e.g. item 202 FIG.2) reads in 302 data suitable for determining a utility class of amonitored utility, a facility class of a monitored facility, a temporalclass of the monitored utility, a duration of a monitored time period, asize of the monitored facility and an expected average unit consumptionrate of the monitored utility.

The consumption percentile meter then determines 303 an expectedconsumption of said monitored utility based on said size of saidmonitored facility, said duration of said monitored time period and saidexpected average unit consumption rate of said monitored utility.

The consumption percentile meter then determines 304 an expectedconsumption quantity class of said monitored facility based on saidexpected consumption of said monitored utility.

The consumption percentile meter then queries 306 a cumulativedistribution (CDF) database using said utility class, facility class,temporal class, duration of said monitored time period and said expectedconsumption quantity class of said monitored utility to identifyappropriate cumulative distribution data. As described above, theappropriate cumulative distribution data is based on data from monitoredreference facilities in the same utility class, facility class, temporalclass and expected consumption quantity class as the monitored facility.

The consumption percentile meter then reads in 308 the appropriatecumulative distribution data.

The consumption percentile meter then reads in 310 consumption data forthe monitored utility of the monitored facility for the monitored timeperiod. A person of ordinary skill will understand that the consumptionpercentile meter could alternatively read in the consumption data priorto executing steps 302 to 308 or during the execution of steps 302 to308.

The consumption percentile meter then calculates 312 a normalizedconsumption for the monitored facility using said consumption data andsaid expected consumption of said monitored utility.

The consumption percentile meter then determines 314 a consumptionpercentile for the monitored facility based on the normalizedconsumption and the cumulative distribution data.

The consumption percentile meter then outputs 316 the consumptionpercentile for the monitored facility.

If the consumption percentile meter is monitoring more than onefacility, the consumption percentile meter may check 318 to see if morefacilities need to be monitored and then begin the process again. Theconsumption percentile meter may also multiplex between facilities iftheir monitored time periods overlap so that multiple facilities can bemonitored during said overlapping monitored time periods.

Cumulative Distributions for Facilities of Different ExpectedConsumption Quantity Classes

FIG. 4 is a graph 400 comparing cumulative distributions of referencefacilities in the same utility class, temporal class, and duration ofmonitored time periods but in different expected consumption quantityclasses. The utility class is “lost wages and medical expenses due toon-the-job injuries”. The temporal class is “randomly initiated events”.The duration of the monitored time periods is one year. Cumulativedistributions for a small expected consumption quantity class 404 and alarge expected consumption quantity class 402 are shown. This data isbased on Table M.

The cumulative distribution data for the large expected consumptionquantity class has a moderately long tail 408. The moderately long tailincreases the average of the cumulative distribution so that a facilitywith a normalized consumption of 1.0 (item 406) has a consumptionpercentile of about 60 (item 407). This means that 60% of the referencefacilities used to determine the cumulative distribution data had anormalized consumption that was less than the average for all of thereference facilities.

The cumulative distribution data for the small expected consumptionquantity class has a very long tail 414. For this expected consumptionquantity class, a monitored facility that has a normalized consumptionof 1.0 would have a consumption percentile of about 78 (item 409). Thismeans that 78% of the reference facilities in this class have anormalized consumption that is less than average.

Another characteristic of the cumulative distribution data for the smallexpected consumption quantity class is that 20% of the facilities havezero normalized consumption. This corresponds to no accidents during aone year monitored time period. Facilities with zero normalizedconsumption have an indeterminate consumption percentile (item 412)between 0 and 20.

Another characteristic of the cumulative distribution data for the smallexpected consumption quantity class is that there is a region ofuncertain consumption percentiles 413. This may be due to uncertaintiesin the normalized consumption of a monitored facility. As described inmore detail below, one source of uncertainty is utility consumption thatoccurs in a tail period after the end of a monitored time period that isnonetheless attributable to the consumption of the monitored utilityduring the monitored time period. Ranges of indeterminate and uncertainconsumption percentiles can impact billing methods based on consumptionpercentile.

Billing Methods Based on Consumption Percentile

Billing methods for utilities consumed by a monitored facility can bebased on consumption percentile. Said billing methods may be implementedon said computerized facility billing module 260 (FIG. 2). FIG. 5 showsa graph 500 of an exemplary normalized billing rate 510 versusconsumption percentile 511. This is referred to herein as a “normalizedrate curve”. The normalized billing rate starts out at a minimum value(item 506) for a consumption percentile of 0 and increases linearly to amaximum value (item 508) at a consumption percentile of 100. The amountcharged for the utility is set equal to the normalized billing ratetimes the expected consumption for the monitored utility times the unitprice of the monitored utility. As described above, the expectedconsumption of the monitored utility is the expected average unitconsumption rate of the monitored utility times the size of themonitored facility times the duration of the monitored time period.

A billing method based on consumption percentile has advantages forfacilities that have fluctuations in utility consumption that are bothpartially in control of the facility and partially out of control of thefacility. For example, a facility with a backup electric generatorcannot control the weather or other external factors that could lead toa loss of electric power and hence consumption of fuel for said backupgenerator. The facility does have control, however, of the size of thebackup generator and hence which systems will stay powered within thefacility in the event of a loss of electric power. With percentileconsumption billing, a facility with good practices relative toreference facilities will have a lower consumption percentile and hencepay less. This will provide incentive to maintain best practices. On theother hand, even if a facility has best practices, it still mightoccasionally experience an unusually high number of electric powerlosses and hence have very high backup fuel consumption. With percentileconsumption billing, said facility would be protected againstexcessively high utility costs since its consumption percentile iscapped at 100. This leads to a capped charge no matter how high theactual consumption is.

In order to make sure that the supplier of the utility receives enoughpayment for the consumed utility from all supplied facilities, the area510 under the curve 500 of normalized billing rate versus consumptionpercentile should be at least 100 or greater. This corresponds to anarea under the curve of 1.0 or greater if the units of consumptionpercentile are fractional (i.e. 0 to 1.0).

An alternative billing method is to charge a facility for the facility'sexpected consumption 502 prior to a monitored time period. At the end ofthe monitored time period, the consumption percentile is determined fromthe monitored utility consumption and then used to determine a refund512 or surcharge 514 to be levied on the facility. An example of thistype of alternative billing method is described below with reference toFIG. 9.

FIG. 6 shows a different normalized billing rate curve 600 percentilewith low 602 and high 606 plateaus. Plateaus may be provided inconsumption percentile ranges that are either indeterminate 612 oruncertain 618. Plateau regions for ranges of indeterminate consumptionpercentile are useful since the actual consumption percentile in saidranges cannot be determined. Thus, a charge will be constant over arange of indeterminate consumption. Plateau regions in ranges ofuncertain consumption percentile are useful to minimize billing disputeswith facilities where the accuracy of the utility consumption data maybe in dispute. The charge will be constant over the range of uncertainconsumption percentile so there is no need to dispute the accuracy ofthe utility consumption data.

The low plateau and high plateau regions are connected by a linearregion 604 over a range of consumption percentiles. The parameters ofthe normalized rate curve, such as plateau values, consumptionpercentile ranges, and slope of linear region, can be selected so thatthe area (item 610) under the curve is set to 100 or greater. The samelinear region can be used for a plurality of facilities wherein eachfacility is associated with a different cumulative distribution.

FIG. 7 shows a different normalized billing rate curve 700 that has afirst linear region 702 followed by a sharp increase 704 which tapersinto a plateau 706. The sharp increase is at a consumption percentilethat is above the median (item 708). In this example, the sharp increaseoccurs at a consumption percentile of about 65. Thus 65% of thefacilities will get a discount relative to the average. Facilities witha consumption percentile above 65, however, are subject to significantsurcharge. The surcharge is curved so that even if a facility has aconsumption percentile above 65, there is still incentive to not gohigher. The final plateau makes sure that no facility is excessivelycharged even if its normalized consumption is quite high.

Any number of alternative normalized billing rate curves can bedesigned. A computer implemented system can be provided to allow adesigner to create alternative designs. The computerized design systemmay automatically adjust curve parameters so that the area under thecurve 710 is 100 or some greater value. The curves may be displayed on ascreen with provision made for the designer to modify the curve. Agreater value than 100 for the area under the curve may be used ifprofitability, return on investment, or time value of money (e.g.extended payment plans) are built into the billing method. An area underthe curve of less than 100 may be appropriate in certain circumstances,such as designing systems for introductory offers.

Anticipated changes in facility behavior in response to differentbilling rate designs may be incorporated into the design system so thatfeedback between the billing rate design and the impact of said changesin facility behavior on the cumulative distributions can be modeled.This feedback on the cumulative distributions can occur when data frommonitored facilities is fed back into the cumulative distributiondatabase as described with reference to item 205 of FIG. 2.

A normalized rate curve can improve the physical performance of acomputerized billing system. The same normalized rate curve can be usedfor different cumulative distributions. Thus, the computation time tocalculate a billing rate is reduced when the same system has to bill fora plurality of cumulative distributions that are required for aplurality of monitored facilities.

Consumption Percentile Forecasting

FIG. 8 is a flow chart for a computer implemented method 800 ofconsumption percentile forecasting using a statistical facility monitor.A facility is to have a utility monitored for a monitored time periodusing a consumption percentile meter. The monitored time period, forexample, might be three years. The facility, however, will be billed forintermediate payments during the monitored time period. This avoids thefacility being presented with a large bill at the end of the monitoredtime period. In order to provide intermediate billing of the expectedfinal charge, however, the system for statistical facility monitoringmust provide intermediate forecasts of what the expected consumptionpercentile will be at the end of the monitored time period.

The system for statistical facility monitoring first reads in 802 datasuitable for determining the classes and the expected average unitconsumption rate of the monitored facility. The data suitable forclasses is described in more detail above with reference to item 302 ofFIG. 3.

The system then determines 803 an expected consumption of the monitoredutility. This is described in more detail with reference to item 303 ofFIG. 3.

The system then determines 804 an expected consumption quantity class ofthe monitored facility. This is described in more detail with referenceto item 304 of FIG. 3.

The system then queries 806 a CDF database using the appropriate classesfor the monitored facility to determine the appropriate cumulativedistribution data. This is described in more detail with reference toitem 306 of FIG. 3.

The system then reads in 808 the appropriate cumulative distributiondata. This is described in more detail with reference to item 308 ofFIG. 3.

The system then divides 809 the monitored time period into two or moremetered periods.

The system then reads in 810 the current utility consumption for thecurrent metered period.

At the end of the current metered period, the system will add 812 thecurrent consumption to any prior consumption for prior metered periodsto give an updated consumption.

The system will then forecast 813 the expected consumption for allfuture metered periods that have not been monitored yet. The forecastmay be the expected average unit consumption rate of the monitoredutility times the size of the monitored facility times the duration ofthe future metered periods. The system, therefore, assumes that themonitored facility will have average consumption for future meteredperiods irrespective of the level of monitored consumption up to thatpoint. The system may alternatively adjust the expected average unitconsumption rate used for forecasting future consumption based on thelevel of monitored consumption up to that point. If the monitoredconsumption is below average, the expected average unit consumption ratefor future metered periods may be reduced. If the monitored consumptionis above average, the expected average unit consumption rate for futuremetered periods may be increased. In the insurance industry, the factorused to make this kind of adjustment is known as an “experiencemodifier”. A similar factor can be applied to the hospital backupgenerator system as described above. A similar factor can also beapplied to any physical system with randomly initiated utilityconsumption.

The system will then add 814 the forecasted consumption to the updatedconsumption to give the expected consumption for the entire monitoredtime period.

The system will then calculate 816 a normalized expected consumption forthe entire monitored time period.

The system will then use 818 the cumulative distribution data todetermine an expected consumption percentile for the entire monitoredtime period.

The system will then output 820 the expected consumption percentile forthe entire monitored time period.

The system will then determine 822 if the current metered period is thefinal metered period. If not, then the system defines 824 the nextmetered period as the current metered period and repeats steps 810 to822.

Referring back to step 822, when the current metered period is the finalmetered period, the system may then check 823 to see if there is a tailperiod. A tail period can occur when there is expected utilityconsumption that will occur after the end of the monitored time period.As described above, a backup generator might run for a period of timethat goes beyond the monitored time period and into a tail period. Whenit does, the system will forecast 825 the consumption for the tailperiod. The system will then return to step 814 and add the forecastedconsumption for the tail period to the updated consumption to give theexpected consumption for the entire monitored time period. Theconsumption percentile meter, therefore, may provide a consumptionpercentile for a monitored time period even though there is futureunmeasured consumption that will occur in a tail period. This isespecially useful for billing systems where there might be very longtail periods, such as workers' compensation insurance. As describedabove with reference to the plateau region 606 of FIG. 6, a normalizedbilling rate curve can be constructed so that it is insensitive touncertainties in measured consumption. Billing rate curves with plateaufeatures, therefore, may be particularly useful when there are long tailperiods and hence significant uncertainties in a particular monitoredfacility's future consumption.

Referring back to step 823, when there is no additional tail period, thesystem ends or proceeds to monitor another facility. Similar to theprocess described in FIG. 3, the system may multiplex between monitoredfacilities if their monitored time periods overlap.

Intermediate Charges

Forecasted consumption percentiles can be used by a billing system forintermediate billing of a monitored utility. The basic idea is to dividea monitored time period into two or more metered periods. At thebeginning of each metered period, a forecast is made of what theconsumption percentile will be at the end of the monitored time period.The forecasted consumption percentile is used to determine a forecastedtotal charge for the monitored utility over the entire monitored timeperiod. The billing system then determines an intermediate charge forthe current metered period based on the duration of the current meteredperiod, the time left in the monitored time period and any earlierintermediate charges already paid. This is illustrated in FIG. 9.

FIG. 9 illustrates 900 a method for intermediate billing. Totalforecasted charges for a monitored utility for an entire monitored timeperiod (e.g. items 912, 914, 916 and 918) are shown versus time. Amonitored time period 910 is subdivided into sequential metered periods(e.g. item 911). The monitored time period has a beginning 901 and anend 903. Each metered period also has a beginning (e.g. item 905) and anend (e.g. item 907). In this example, there are three metered periods.Any number of metered periods, however, can be used. The metered periodsdo not have to be the same duration but can be any duration appropriateto the monitored utility.

At or before the beginning of the first metered period, an initialforecast is made of the total expected charge 912 for the monitoredutility over the entire monitored time period. The initial forecast ofthe total expected charge may be based on an initial forecasted value ofwhat the consumption percentile will be for the entire monitored timeperiod. An initial forecasted value of 50 (i.e. average consumption) issuitable. A normalized billing rate may then be determined using theinitial forecasted value of the consumption percentile and a normalizedbilling rate curve. One can use one of the normalized billing ratecurves illustrated in FIGS. 5 to 7 or any other normalized billing ratecurve subject to the conditions set forth above (e.g. area under thecurve of about 100). The initial forecast of the total expected chargefor the monitored utility for the monitored time period, therefore, isthe normalized billing rate times the expected average unit consumptionrate of the monitored utility times the size of the monitored facilitytimes the duration of the monitored time period times the unit price ofthe monitored utility.

The system then calculates an intermediate charge 902 for the upcomingfirst metered period. The intermediate charge is equal to the initialforecast of the total expected charge 912 less any prior payments timesthe duration of the first metered period divided by the duration of allof the remaining metered periods in the monitored time period. Theremaining metered periods include the first metered period. In thisexample, if the initial forecast of the total expected charge was $100,the intermediate charge for the upcoming first metered period would beabout $33.

At the end of the first metered period, the process is repeated for thesecond metered period. An updated forecast is made of the total expectedcharge 914 at the end of the monitored time period. The updated forecastof the total expected charge is based on an updated forecast of theconsumption percentile for the entire monitored time period. The updatedforecast of the consumption percentile is based on an updated forecastof the total consumption at the end of the monitored time period. Theupdated forecast of the total consumption at the end of the monitoredtime period is equal to the measured utility consumption as of the endof the first metered period plus the expected consumption for theremaining metered periods.

In the example shown, the updated forecast at the end of metered period1 of the total expected charge 914 for the entire monitored time periodhas fallen relative to the initial forecast of the total expected charge912 for the entire monitored time period. This indicates that themonitored facility had lower than average utility consumption during thefirst metered period. The intermediate charge 904 for the second meteredperiod, therefore, will be less than the intermediate charge 902 for thefirst metered period. Thus, the monitored facility has an immediatereward for having less than expected utility consumption during thefirst metered period.

The process is repeated at the end of metered period 2. The totalexpected charge 916 for the entire monitored time period has againfallen. This again indicates that the measured utility consumptionduring metered period 2 was less than average. The intermediate charge906 for period 3, therefore, is less than the intermediate charge forperiod 2.

At the end of the monitored time period, a forecast is made of theexpected consumption during a tail period. This gives a final value ofthe consumption percentile for the entire metered period and hence afinal charge 918 for the monitored utility for the entire monitored timeperiod. In this example, the forecasted utility consumption for the tailperiod was less than average so a refund 908 is given to the monitoredfacility.

In some situations, it may be desirable to avoid giving refunds if onlyto simplify the computer systems used to implement the billing process.This can be achieved by reducing the earlier intermediate charges sothat it is expected that even if a monitored facility consistently haslower than average consumption, there will still be a final charge. Thiscan benefit the facility since charges are deferred.

Statistical Facility Event Monitor

Referring again to FIG. 2, in an alternative embodiment, the consumptionpercentile meter 202 may count randomly initiated events 252 in acertain event class during a monitored time period. The randomlyinitiated events cause the monitored facility 210 to consume themonitored utility. The consumption percentile meter then computes acumulative distribution function for the randomly initiated events andthen determines an event percentile for the monitored facility. Theevent percentile is based on the fraction of reference facilities (e.g.items 220, 230, 240) that have a number of randomly initiated events inthe event class during a reference time period that are less then thecounted number of randomly initiated events that occur to the monitoredfacility during the monitored time period. The monitored time period andthe reference time period have about the same duration.

The event class is a type of randomly initiated event. For example, fora monitored facility that is subject to intermitted outages of electricutility power, an event class might be all intermittent outages thatcause a diesel backup generator to turn on. A given event classes mightbe part of a larger meta-class. For example, a meta-class might includeall events that cause a loss of electric utility power even those thatare too short to cause the diesel backup generator to turn on. Theshorter events might only cause a small discharge from a battery backupsystem.

An expected number of randomly initiated events in an event class can bedetermined from an expected average consumption rate of the monitoredutility by the monitored facility and an expected incremental number ofrandomly initiated events per unit consumption of the monitored utility.For example, a large number of reference facilities could be monitoredto count the number of randomly initiated events that occur as well asthe total consumption of the monitored utility due to the randomlyinitiated events. For example, if the monitored reference facilitiescollectively consume 10 million gallons of diesel fuel afterexperiencing 10 thousand power outages that cause the diesel generatorsto turn on, then the expected incremental number of randomly initiatedevents per unit consumption of the monitored utility is 1 event per1,000 gallons of diesel fuel. If the monitored facility has an expectedconsumption of 1,000 gallons of diesel fuel per year, then the expectednumber of randomly initiated events in the event class during themonitored time period is 1.

It has been found by experiment that in certain situations, thecumulative distribution function for the randomly initiated events canbe described by one or more of a Poisson distribution, a Negativebinomial distribution or a combined Poisson and Gamma distribution.Hence it is only necessary to determine the parameters of thesedistributions in order to generate the cumulative distribution. Forexample, if the distribution is Poisson, then the only parameter that isneeded is the expected number of randomly initiated events in the eventclass during the monitored time period. This ability to calculate thecumulative distribution function in this application improves thefunctioning of the digital processor since it is no longer necessary tosearch the cumulative distribution function database 204 for thecumulative distribution function and then transfer of a cumulativedistribution function to the consumption percentile meter 202.

Method for Event Percentile Metering

FIG. 10 is a flow chart 1000 of a method for event percentile metering.A computer implemented event percentile meter is configured to:

-   -   a) read in 1002 by an input device, data suitable for        determining:        -   i) an event class of randomly initiated events that cause a            monitored facility to consume a monitored utility;        -   ii) a duration of a monitored time period;        -   iii) an expected average consumption rate of the monitored            utility by the monitored facility due to said randomly            initiated events in said event class; and        -   iv) an expected incremental number of said randomly            initiated events in said event class that will occur to said            monitored facility per unit consumption of said monitored            utility by said monitored facility due to said randomly            initiated events in said event class;    -   b) determine 1003 by a digital processor, an expected total        number of randomly initiated events in said event class that        will be experienced by said monitored facility during said        monitored time period based on:        -   i) said duration of said monitored time period;        -   ii) said expected average consumption rate of said monitored            utility by said monitored facility due to said randomly            initiated events in said event class; and        -   iii) said expected incremental number of said randomly            initiated events in said event class that will occur to said            monitored facility per unit consumption of said monitored            utility by said monitored facility due to said randomly            initiated events in said event class;    -   c) determine 1004 by said digital processor, a cumulative        distribution function (CDF) for said randomly initiated events        in said event class based at least in part on said expected        total number of randomly initiated events in said event class;    -   d) count 1006 by said input device, a number of said randomly        initiated events in said event class that occur to said        monitored facility during said monitored time period;    -   e) determine 1008 by said digital processor, an event percentile        for the monitored facility based on said counted number of        randomly initiated events and said CDF; and    -   f) output 1010 on a computer screen, said event percentile for        the monitored facility.

The monitored utility could be one or more of energy, materials, labor,capital costs, monetary expenses, or any other consumable utility.

The method 1010 then determines 1012 if there are more facilities to bemonitored. If so, then the method is executed for the next monitoredfacility. If not, then the method ends 1014.

Alternative Applications

The statistical facility event monitor can be applied to any applicationwhere randomly initiated events cause the consumption of a monitoredutility. In the field of workers' compensation, for example, the eventclass could be an accidental injury to a worker within a monitoredfacility. The monitored utility would then be the medical costs and losttime compensation of said worker due to said accidental injury.

Each one of the workers in a monitored facility may be described by:

-   -   i) an expected average unit consumption rate of said medical        costs and lost time compensation due to accidental injuries; and    -   ii) an expected incremental number of injuries per unit        consumption of said medical costs and lost time compensation.

The expected total number of randomly initiated events in said eventclass could then be determined at least in part from:

-   -   iii) said expected average unit consumption rate of said medical        costs and lost time compensation due to said accidental        injuries; and    -   iv) said expected incremental number of injuries per unit        consumption of said medical costs and lost time compensation.

The expected number of injuries per unit consumption of said medicalcosts and lost time compensation can be determined in part by a hazardgroup of a job classification of a worker. The National Council onCompensation Insurance, for example, annually aggregates accidentalinjury data from a relatively large number (e.g. 4 million) of injuriesto insured persons in different job classifications. It then publishesto its members average expected insurance losses (i.e. medical costs,lost time compensation as well as other expenses) per unit of payrollfor the different job classifications. It also publishes to its members,hazard group ratings for the different job classifications.

Referring to FIG. 2, the graphs 225, 235, 245 can represent the costs ofworker injuries for different reference facilities 220, 230, 240 thatall have the same average unit consumption rates 226, 236, 246 ofmedical costs and lost time compensation. The differences in themagnitudes 224, 234, 244 and frequencies of the injury events indicatedifferences in the hazard groups of the workers in the differentreference facilities. Thus, workers with a job classification that has alow hazard rating would have a relatively large number of low severityaccidents 224 for a given average unit consumption rate 226 of medicalcosts and lost time compensation. Workers with a job classification in amedium hazard group would have a medium number of medium severityaccidents 234 for the same average unit consumption rate 236 of medicalcosts and lost time compensation. Workers with a job classification in ahigh hazard group would have a small number of high severity accidents244 for the same average unit consumption rate 246 of medical costs andlost time compensation. As used herein, “severity” is the total dollarvalue of the medical costs and lost time compensation provided to theworker. Severity may also include other costs associated with processinga workers' compensation insurance claim, such as the cost of litigationshould there be a dispute between an injured worker and the insurancecompany as to which medical costs and lost time compensation areproperly attributed to the on-the-job injury.

The expected total number of randomly initiated events in the eventclass of worker injuries for each worker can be determined bymultiplying the expected average unit consumption rate of said medicalcosts and lost time compensation due to said accidental injuries foreach worker by said expected incremental number of injuries per unitconsumption of said medical costs and lost time compensation based onthe hazard group each worker's job classification. The expected totalnumber of randomly initiated events in the event class for the monitoredfacility can be determined from the sum of the expected number ofrandomly initiated events for each worker.

As indicated above, because these are discrete events, the cumulativedistribution function for the events can be calculated using a Poissondistribution or other discrete event distribution.

It has been surprisingly found that reasonably accurate estimates of theexpected incremental number of injuries per unit consumption of saidmedical costs and lost time compensation can be determined from arelatively small data set of only 10,000 injuries. Thus, the presentmethod for implementing a statistical facility event monitor furtherimproves the performance of the digital processors performing thecalculations by reducing the amount of data that needs to be processedfrom millions of injuries to determine a cumulative distribution ofutility consumption (e.g. Table M) to thousands of injuries to determinea cumulative distribution of events.

Meta-Class Calculations

In some situations, data from a large number of reference facilities maybe readily available for events in a meta-class but not for events in aparticular event class of interest. For example, in the field ofworkers' compensation, published data on the expected workers'compensation losses per unit of payroll for different jobclassifications for all types of accident events is readily available. Adesigner of a statistical facility event monitor, however, may only beinterested in accidental injuries that result in lost time compensation(i.e. lost time accidents) and not accidents that only result in medicalexpenses (i.e. medical only accidents). The medical only accidents arelarge in number, small in severity and can unduly skew the total eventcount, particularly for different workers with different hazard ratingsfor their jobs. Hence the designer may wish to disregard them. This issimilar to the electrical utility example above where a designer maydesire to disregard the relatively large number of small electricalutility power outages that only result in a battery discharge but not indiesel generator usage.

In the case of workers' compensation, it has been surprisingly found byexperiment that with data from about 10,000 worker injuries thatincludes event classes (e.g. medical only and lost time) as well asseverity (medical losses, lost time compensation, and expenses), one candetermine an expected incremental number of randomly initiated events inan event class (e.g. lost time accidents) per unit consumption of amonitored utility by a monitored facility due to all randomly initiatedevents in a meta-event class (e.g. both medical only and lost timeaccidents). This discovery substantially improves the performance of acomputer implemented statistical facility event monitor since it is notnecessary to reprocess the data for millions of accidents in order todetermine expected workers' compensation losses just for lost timeaccidents in different job classifications.

Thus, in a computer implemented event percentile meter, when:

-   -   a) an event class is a member of a meta-event class;    -   b) the read-in data comprises:        -   v) an expected average unit consumption rate of a monitored            utility by a monitored facility due to randomly initiated            events in said meta-event class; and        -   vi) an expected incremental number of randomly initiated            events in an event class per unit consumption of said            monitored utility by said monitored facility due to randomly            initiated events in said meta-event class    -   said computer implemented event percentile meter may be        configured to:        -   vii) alternatively determine by said digital processor, said            expected total number of randomly initiated events in said            event class that will be experienced by said monitored            facility during said monitored time period based on:            -   1) said duration of said monitored time period;            -   2) said expected average unit consumption rate of said                monitored utility by said monitored facility due to said                randomly initiated events in said meta-event class; and            -   3) said expected incremental number of randomly                initiated events in said event class per unit                consumption rate of said monitored utility by said                monitored facility due to randomly initiated events in                said meta-event class.                Continuing with the diesel backup generator example:    -   a) the monitored facility comprises a battery backup and a        diesel electric generator;    -   b) the meta-event class is any accidental loss of electric        utility power to said monitored facility; and    -   c) the event class is an accidental loss of electric utility        power to said monitored facility that causes said diesel        electric generator to turn on and provide electric power to said        monitored facility.

Continuing with the workers' compensation example:

-   -   a) the monitored facility comprises one or more workers;    -   b) the meta-event class is any accidental injury of a worker        that causes a workers' compensation claim to be filed; and    -   c) the event class is an accidental injury of a worker that        causes said worker to receive compensation for lost time on the        job.

Computer Implemented Statistical Facility Event Billing Module

Similar to the statistical facility monitor described above, thestatistical facility event monitor may comprise a computer implementedfacility billing module configured to provide a charge for saidmonitored utility based on said event percentile for said monitoredfacility. The charge may have a linear relationship to the eventpercentile for said monitored facility over a range of eventpercentiles. The statistical facility event monitor may also be adaptedto receive by manual input at least a portion of the counted number ofrandomly initiated events in an event class experienced by a monitoredfacility during a monitored time period.

A surprising advantage of the statistical facility event billing moduleis that it is no longer necessary to forecast how much consumption ofthe monitored utility there might be in a tail period. It's onlynecessary to determine if a randomly initiated event occurred in theevent class. There may still be some delay between when a randomlyinitiated event occurs and when it is entered into the event percentilemeter, but it has been found by experiment that the delay is muchshorter than the delay due to ongoing consumption of the monitoredutility in the tail period. When the statistical facility event billingmodule, therefore, is used in the billing of retrospective insurancepremiums, an account may be closed more quickly and a final billpresented to an insured without having to wait for all consumption ofthe utility in a tail period or forecasting how much consumption theremight be.

CONCLUSION

While the disclosure has been described with reference to one or moredifferent exemplary embodiments, it will be understood by those skilledin the art that various changes may be made and equivalents may besubstituted for elements thereof without departing from the scope of thedisclosure. In addition, many modifications may be made to adapt to aparticular situation without departing from the essential scope orteachings thereof. Therefore, it is intended that the disclosure not belimited to the particular embodiment disclosed as the best modecontemplated for carrying out this invention.

We claim:
 1. A computer implemented statistical facility event monitordirected to the practical application of eliminating the need to waitfor an end of a tail period subsequent to a monitored time period inorder to determine a consumption of a monitored utility attributable torandomly initiated events occurring during said monitored time periodwherein said statistical facility event monitor comprises a computerimplemented event percentile meter comprising a monitor for countingsaid randomly initiated events that cause said monitored facility toconsume said monitored utility, an input device, an output device, and adigital processor wherein said computer implemented event percentilemeter is configured to: a) read in by said input device, data suitablefor determining: i) an event class of said randomly initiated eventsthat cause said monitored facility to consume said monitored utility;ii) a duration of said monitored time period; iii) an expected averageconsumption rate of the monitored utility by the monitored facility dueto said randomly initiated events in said event class; and iv) anexpected incremental number of said randomly initiated events in saidevent class that will occur to said monitored facility per unitconsumption of said monitored utility by said monitored facility due tosaid randomly initiated events in said event class; b) determine by saiddigital processor, an expected total number of randomly initiated eventsin said event class that will be experienced by said monitored facilityduring said monitored time period based on: i) said duration of saidmonitored time period; ii) said expected average consumption rate ofsaid monitored utility by said monitored facility due to said randomlyinitiated events in said event class; and iii) said expected incrementalnumber of said randomly initiated events in said event class that willoccur to said monitored facility per unit consumption of said monitoredutility by said monitored facility due to said randomly initiated eventsin said event class; c) determine by said digital processor, acumulative distribution function (CDF) for said randomly initiatedevents in said event class based at least in part on said expected totalnumber of randomly initiated events in said event class; d) receive bysaid input device from said monitor for counting said randomly initiatedevents, a number of said randomly initiated events in said event classthat occurred to said monitored facility during said monitored timeperiod; e) determine by said digital processor, an event percentile forthe monitored facility based on said counted number of randomlyinitiated events and said CDF; and f) present by said output device,prior to said end of said tail period, a bill comprising a charge atleast in part for said consumption of said monitored utility during saidtail period attributable to said randomly initiated events occurringduring said monitored time period wherein said charge is based on saidevent percentile.
 2. The statistical facility event monitor of claim 1wherein said monitored utility is one or more of energy, materials,labor, capital costs or monetary expenses.
 3. The statistical facilityevent monitor of claim 1 wherein: a) said event class is an accidentalloss of electric utility power provided to said monitored facility; andb) said monitored utility comprises fuel to run a backup generator toreplace said loss of said electric utility power.
 4. The statisticalfacility event monitor of claim 1 wherein: a) said event class is anaccidental injury to a worker within said monitored facility; and b)said monitored utility comprises medical costs and lost timecompensation of said worker due to said accidental injury.
 5. Thestatistical facility event monitor of claim 4 wherein: a) said monitoredfacility comprises one or more workers; b) each of said one or moreworkers is described by: i) an expected average unit consumption rate ofsaid medical costs, lost time compensation and other expense due toaccidental injuries; and ii) an expected incremental number of injuriesper unit consumption of said medical costs, lost time compensation andother expenses; and c) said expected total number of randomly initiatedevents in said event class is determined at least in part from: i) foreach of said one or more workers, said expected average unit consumptionrate of said medical costs, lost time compensation and other expensesdue to said accidental injuries; and ii) said expected incrementalnumber of injuries per unit consumption of said medical costs, lost timecompensation and other expenses.
 6. The statistical facility eventmonitor of claim 5 wherein said expected number of injuries per unitconsumption of said medical costs, lost time compensation and otherexpenses is determined in part by a hazard group of a job classificationof a worker.
 7. The statistical facility event monitor of claim 1wherein: a) said event class is a member of a meta-event class; b) saidread-in data comprises: i) an expected average unit consumption rate ofsaid monitored utility by said monitored facility due to randomlyinitiated events in said meta-event class; and ii) an expectedincremental number of randomly initiated events in said event class perunit consumption of said monitored utility by said monitored facilitydue to randomly initiated events in said meta-event class; and c) saidcomputer implemented event percentile meter is configured to: i)alternatively determine by said digital processor, said expected totalnumber of randomly initiated events in said event class that will beexperienced by said monitored facility during said monitored time periodbased on: 1) said duration of said monitored time period; 2) saidexpected average unit consumption rate of said monitored utility by saidmonitored facility due to said randomly initiated events in saidmeta-event class; and 3) said expected incremental number of randomlyinitiated events in said event class per unit consumption rate of saidmonitored utility by said monitored facility due to randomly initiatedevents in said meta-event class.
 8. The statistical facility eventmonitor of claim 7 wherein: a) said monitored facility comprises abattery backup and a diesel electric generator; b) said meta-event classis any accidental loss of electric utility power to said monitoredfacility; and c) said event class is an accidental loss of electricutility power to said monitored facility that causes said dieselelectric generator to turn on and provide electric power to saidmonitored facility.
 9. The statistical facility event monitor of claim 7wherein: a) said monitored facility comprises one or more workers; b)said meta-event class is any accidental injury of a worker; and c) saidevent class is an accidental injury of a worker that causes said workerto receive compensation for lost time on the job.
 10. The statisticalfacility event monitor of claim 1 wherein said charge has a linearrelationship to said event percentile for said monitored facility over arange of event percentiles.
 11. The statistical facility event monitorof claim 1 which is adapted to receive by manual input at least aportion of said counted number of randomly initiated events in saidevent class.